Electromagnetism & Resonant Recognition Model
The interaction between biomacromolecules is dependent of their electromagnetic resonant properties
The Resonant Recognition Model is a very interesting model, now with various experimentally verified predictions and celebrated, that surpass the impossible classical supposition that interactions between biomolecules is reached only by brownian-motion derived chance proposing an electromagnetic interaction mode between them that also serve for their distant recognition. ...
This theory predicts that macromolecular activity is based on electromagnetic resonances, the delocalised electrons moving along macromolecular (protein, DNA, RNA) backbone-like helical structure, can produce electromagnetic radiation, absorption and resonance with the spectral characteristics that correspond to the energy distribution along the macromolecule. The initial calculus is done as described in :
" All proteins, DNA and RNA can be considered as a linear sequence of their constitutive elements: amino acids or nucleotides. The RRM model interprets this linear information as a numerical series by assigning each amino acid a physical parameter representing the energy of delocalised electrons of each amino acid and then transforming this numerical series into the frequency domain using Fourier Transform."
Irena Cosic and others discovered that each specific biological function within the protein or DNA is characterized by one frequency, that all protein sequences with the common biological function have a common frequency component related to the protein biological function, and that proteins and their targets have the same characteristic frequency in common . Those protein and DNA resonances are within infra-red, visible and small portion of ultraviolet light, and functionalities of these molecules correspond to frequencies that can be grouped :
" It can be observed from the calculated RRM frequencies, that there are interesting groupings of biological functions into functional super families. For example, it can be observed that protein and DNA functions, that are related to uncontrolled cell growth super family (like oncogenes, antitumor agents, TNFs, etc.), are all within the frequency range frequency range between 0.031 and 0.054."
Other superfamilies with elements that works each other in a concrete frequency range can alaso be found, as for examples a superfamily related to viral and bacterial infections, o related to the controlled growth o to the enzyme activity .
In a review by W. Jaross  some aspects of this resonance recognition of the vibration patterns by proteins as precondition for molecular interactions are discussed:
" The molecular vibration patterns of structure-forming macromolecules in the living cell create very specific electromagnetic frequency patterns which might be used for information on spatial position in the three-dimensional structure as well as the chemical characteristics. Chemical change of a molecule results in a change of the vibration pattern and thus in a change of the emitted electromagnetic frequency pattern. These patterns have to be received by proteins responsible for the necessary interactions and functions. Proteins can function as resonators for frequencies in the range of 1013-1015 Hz. The individual frequency pattern is defined by the amino acid sequence and the polarity of every amino acid caused by their functional groups. If the arriving electromagnetic signal pattern and the emitted pattern of the absorbing protein are matched in relevant parts and in opposite phase, photon energy in the characteristic frequencies can be transferred resulting in a conformational change of that molecule and respectively in an increase of its specific activity."
The same author proposes a novel function for these informational signals in the substitution of structural elements in the three-dimensional space of the cell, being molecular vibration patterns of those macromolecules which have to be exchanged recognized by molecules in the Golgi via resonance of the electromagnetic fingerprints , to overcome intracellular distances he invoked elements like microtubules or water ordering (both with specific sections  in this web and being important parts of any electromagnetic mind theory) :
" The oscillation of polarized structural elements such as cell membrane rafts or microtubules (12, 25-27) might be able to produce appropriate carrier frequencies able to overcome intracellular distances. The frequencies of MT oscillations have been found to be in the range from THz down to KHz (10, 11). The MT structure itself could also be the base for forwarding the IR-photons with a minimal loss of energy if they are used as waveguides. The quasi-crystalline ordered water molecules inside and outside of the charged tubular structure of the MT could promote that (see Funk, this issue)."
The theory has been corroborated by some experimental findings, for example Murugan et al.  shows that evola strains that are lethal and not lethal have different electromagnetic emissions (that, by the way, also are named biophotonic emissions because of their wavelengths within the visible or near-visible light band and there is a big section  dedicated to biophotons in this web), and that those emissions can be calculated very accurately (with about 10 nm of error margin) using the Resonant Recognition Model. Also it has been predicted processes involved in the interactions of Zika virus (ZIKV) with the human host suggesting the exchange of electromagnetic radiation at the frequencies of 601.8nm (yellow light) and 1203.6nm (near infrared) during ZIKV envelope protein with the AXL receptor in the human tissue . In this sense is also interesting to note fruitful attempts to detect Hepatitis C Virus  based on electromagnetic detection of it’s resonant frequency (as predicted by Cosic).
It has been found also that peculiar processes in biological systems that are dependent on ambient temperature are a consequence of how temperature affects the frequencies, derived from RRM, that characterize the function of specific proteins involved in the processed that have been altered :
" Here, we have presented number of very different biological processes that are related to temperature, including recovery of critical mutation within CFTR proteins related to cystic fibrosis, temperature induced sex determination in alligators, heat shock protein temperature characteristics, temperature induced growth, mammal skin temperature electromagnetic radiation, as well as temperature of protein denaturalisation. All these examples are related to RRM characteristic frequencies, either through RRM characteristics of relevant proteins or through electromagnetic radiation that can be related to RRM."
In  genes and protein related to the development of breast cancer are analyzed under the scope of RRM, being this capable not only to analyze protein biological functions/interactions but to predict bioactive mutations. Also the fluctuating wavelengths of biophotonic emissions (or ultraweak photon emissions (UPE)) from stressed cancer cells can be calculated based on this model .
Protein interactions themselves have been also viewed through RRM, for example with the PKCζ and PKMζ brain specific protein kinases  (that have a role in memory consolidation mechanisms):
" PKCζ can be viewed as a resonance system emitting/receiving infrared light 3190 nm. PKMζ is much more active at this main wavelength, and also is tuned to near infrared at 913 nm. The regulatory and hinge domains are tuned to yellow light (609 nm). At the same time, they do interact with PKMζ through infrared light 3190 nm."
Even complete cellular signaling pathways can be described in this electromagnetic resonance terms, Karbowki et al.  wrote for the classic JAK–STAT signaling pathway:
" Several experimental studies have verified the predicted peak wavelength of photons within the visible or near-visible light band for specific molecules. Here, this concept has been applied to a classic signaling pathway, JAK–STAT, traditionally composed of nine sequential protein interactions. The weighted linear average of the spectral power density (SPD) profiles of each of the eight “precursor” proteins displayed remarkable congruence with the SPD profile of the terminal molecule (CASP-9) in the pathway. These results suggest that classic and complex signaling pathways in cells can also be expressed as combinations of resonance energies."
The same occur for the classic ERK-MAP signaling pathways between the plasma cell membrane and the nucleus :
" Spectral analyses of sequences of pseudopotentials that reflect de-localized electrons of amino acids for the 11 proteins in the pathway were computed. The spectral power density of the terminal protein (cFOS) was shown to be the average of the profiles of the precursor proteins. The results demonstrated that in addition to minute successive alterations in molecular structure wave-functions and resonant patterns can also describe complex molecular signaling pathways in cells. Different pathways may be defined by a single resonance profile."
Cosic, based on her theory also proposed some therapeutic applications, for example to neutralize malaria parasite . In this website a section  is disposed dedicated to low level light therapy and experiments where light is applied to achieve different biological responses, here mechanism by which those effects are provoked can be related in most cases to resonant effects like those predicted by the RRM, moreover there is a subsection  where the listed papers specifically attribute the effects to consequences derived from RRM.
It must be said that there are some alternative resonance models that also are going to be included in this section, for example  propose a model that differs a bit to that of RRM:
" Compared to this previous work, our contribution is twofold. First, whereas the determination of RRM-based hotspots initially requires the computation of the characteristic frequency of a family of proteins, we do not impose such a constraint. Second, rather than a purely DSP-based approach as in , – aimed at detecting local residues associated with the characteristic frequency, we combine DSP tools and mutagenesis principles."
Or the model of  that modified RRM by using the wavelet transform.
The importance of the electromagnetic resonant recognition between macromolecules in regard to an electromagnetic mind theory is that those recognition fields are another layer of the electromagnetic mind, being this more general and multi-layered.
1. Cosic, I., Cosic, D., & Lazar, K. (2015). Is it possible to predict electromagnetic resonances in proteins, DNA and RNA?. EPJ Nonlinear Biomedical Physics, 3(1), 5.
2. Ćosić, I., Pirogova, E., Vojisavljević, V., & Fang, Q. (2006). Electromagnetic properties of biomolecules. FME Transactions, 34(2), 71-80.
3. Cosic, I., Cosic, D., & Lazar, K. (2017). Tesla, Bioresonances and Resonant Recognition Model. In Second International Congress Nikola Tesla-Disruptive innovation.
4. Jaross, W. (2018). Hypothesis on interactions of macromolecules based on molecular vibration patterns in cells and tissues. Front Biosci, 23(3), 940-946.
5. Jaross, W. (2019). Communication of the Cell Periphery with the Golgi Apparatus: A Hypothesis. In The Golgi Apparatus and Centriole (pp. 377-387). Springer, Cham.
6. EMMIND › Water & Electromagnetic Fields › Electromagnetism & Water - Exclusion Zones
7. EMMIND › Endogenous Electromagnetic Fields › Electromagnetism & Microtubules
8. Murugan, N. J., Karbowski, L. M., & Persinger, M. A. (2014). Cosic’s Resonance recognition model for protein sequences and photon emission differentiates lethal and non-lethal ebola strains: Implications for treatment. Open Journal of Biophysics, 5(01), 35.
9. EMMIND › Endogenous Fields & Mind › Biophotons
10. Wright, G. (2018). Zika Virus Viewed Through the Resonant Recognition Model. Unraveling New Avenues for Understanding and Managing a Serious Threat. European Journal of Biomedical Informatics, 14(1).
11. Shiha, G., Samir, W., Amien, A., Bader, H., Abdallah, T., Metwally, A., ... & Sarin, S. (2013). 1174 A Novel Method for Non-Invasive Diagnosis of Hepatitis C Virus Using Electromagnetic Signal Detection: A Multicenter International Study. Journal of Hepatology, 58, S477.
12. Fathy, H., Soliman, L., Attallah, M., El-Sheshtawy, N., & Abd El, A. (2018). Comparative Study between the Conventional Methods and A New Technique using Electromagnetic Waves in Diagnosis and Follow up of Treatment of Hepatitis C Virus Infections. Egyptian Journal of Medical Microbiology. 27(3):21‒27.
13. Cosic, I., & Cosic, D. (2020). Roll of Temperature in Living Systems Analysed Using the Resonant Recognition Model. International Journal of Sciences, 9(04), 40-44.
14. Cosic, I., Cosic, D., & Lazar, K. (2017). Cancer Related BRCA-1 and BRCA-2 Mutations as Analysed by the Resonant Recognition Model. J. Adv. Mol. Biol, 1.
15. Dotta, B. T. (2014). Cosic's Resonant Recognition Model for Macromolecules can be used to Predict and Modify the Fluctuating Wavelengths of Ultraweak Photon Emissions from Stressed Cancer Cells. Biophysical Journal, 106(2), 183a.
16. Valdés García, S., Hernández-Cáceres, J. L., & Palmero Colmenares, D. (2017). Studying Protein kinases PKCζ and PKMζ with the Resonant Recognition Model. Implications for the study of Memory Mechanisms. Revista Cubana de Informática Médica, 17(2), 121-134.
17. Karbowski, L. M., Murugan, N. J., & Persinger, M. A. (2015). Novel Cosic resonance (standing wave) solutions for components of the JAK–STAT cellular signaling pathway: A convergence of spectral density profiles. FEBS open bio, 5(1), 245-250.
18. Cosic, I., Caceres, J. H., & Cosic, D. (2015). Possibility to interfere with malaria parasite activity using specific electromagnetic frequencies. EPJ Nonlinear Biomedical Physics, 3, 1-11.
19. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects
20. EMMIND › Applied Fields - Experimental › Light & Near-Light Effects › Light - Various › Using wavelengths derived from Resonant Recognition Model
21. Nguyen, Q. T., Fablet, R., & Pastor, D. (2011). Protein interaction hotspot identification using sequence-based frequency-derived features. IEEE Transactions on Biomedical Engineering, 60(11), 2993-3002.
22. Liu, X., & Wang, Y. (2007). A modified resonant recognition model to predict protein-protein interaction. Frontiers of Biology in China, 2(3), 268-271.
Very related sections:
↑ text updated: 21/06/2020
↓ tables updated: 05/06/2022
Endogenous Fields & Mind
EM & Resonant Recognition Model